Search

The intensifying global opioid crisis, majorly attributed to fentanyl (FT) and its analogs, has necessitated the development of rapid and ultrasensitive remote/on-site FT sensing modalities. However, current approaches for tracking FT exposure through wastewater-based epidemiology (WBE) are unadaptable, time-consuming, and require trained professionals. Toward developing an extended in situ wastewater opioid monitoring system, we have developed a screen-printed electrochemical FT sensor and integrated it with a customized submersible remote sensing probe. The sensor composition and design have been optimized to address the challenges for extended in situ FT monitoring. Specifically, ZIF-8 metal-organic framework (MOF)-derived mesoporous carbon (MPC) nanoparticles (NPs) are incorporated in the screen-printed carbon electrode (SPCE) transducer to improve FT accumulation and its electrocatalytic oxidation. A rapid (10 s) and sensitive square wave voltammetric (SWV) FT detection down to 9.9 µgL-1 is thus achieved in aqueous buffer solution. A protective mixed-matrix membrane (MMM) has been optimized as the anti-fouling sensor coating to mitigate electrode passivation by FT oxidation products and enable long-term, intermittent FT monitoring. The unique MMM, comprising an insulating polyvinyl chloride (PVC) matrix and carboxyl-functionalized multi-walled carbon nanotubes (CNT-COOH) as semiconductive fillers, yielded highly stable FT sensor operation (> 95% normalized response) up to 10 h in domestic wastewater, and up to 4 h in untreated river water. This sensing platform enables wireless data acquisition on a smartphone via Bluetooth. Such effective remote operation of submersible opioid sensing probes could enable stricter surveillance of community water systems toward timely alerts, countermeasures, and legal enforcement.

Abstract: Quantifying evapotranspiration (ET) is critical to accurately predict vegetation health, groundwater recharge, and streamflow generation. Hillslope aspect, the direction a hillslope faces, results in variable incoming solar radiation and subsequent vegetation water use that drive ET. Previous work in watersheds with a single dominant vegetation type (e.g., trees) have shown that equator‐facing slopes (EFS) have higher ET compared to pole‐facing slopes (PFS) due to higher evaporative demand. However, it remains unclear how differences in vegetation type (i.e., grasses and trees) influence ET and water partitioning between hillslopes with opposing aspects. Here, we quantified ET and root‐zone water storage deficits between a PFS and EFS with contrasting vegetation types within central coastal California. Our results suggest that the cooler PFS with oak trees has higher ET than the warmer EFS with grasses, which is counter to previous work in landscapes with a singule dominant vegetation type. Our root‐zone water storage deficit calculations indicate that the PFS has a higher subsurface storage deficit and a larger seasonal dry down than the EFS. This aspect difference in subsurface water storage deficits may influence the subsequent replenishment of dynamic water storage, groundwater recharge and streamflow generation. In addition, larger subsurface water deficits on PFS may reduce their ability to serve as hydrologic refugia for oaks during multi‐year droughts. This research provides a novel integration of field‐based and remotely‐sensed estimates of ET required to properly quantify hillslope‐scale water balances. These findings emphasize the importance of resolving hillslope‐scale vegetation structure within Earth system models, especially in landscapes with diverse vegetation types.

Methane is a major contributor to anthropogenic greenhouse gas emissions. Identifying large sources of methane, particularly from the oil and gas sectors, will be essential for mitigating climate change. Aircraft-based methane sensing platforms can rapidly detect and quantify methane point-source emissions across large geographic regions, and play an increasingly important role in industrial methane management and greenhouse gas inventory. We independently evaluate the performance of five major methane-sensing aircraft platforms: Carbon Mapper, GHGSat-AV, Insight M, MethaneAIR, and Scientific Aviation. Over a 6 week period, we released metered gas for over 700 single-blind measurements across all five platforms to evaluate their ability to detect and quantify emissions that range from 1 to over 1,500 kg(CH4)/h. Aircraft consistently quantified releases above 10 kg(CH4)/h, and GHGSat-AV and Insight M detected emissions below 5 kg(CH4)/h. Fully blinded quantification estimates for platforms using downward-facing imaging spectrometers have parity slopes ranging from 0.76 to 1.13, with R2 values of 0.61 to 0.93; the platform using continuous air sampling has a parity slope of 0.5 (R2 = 0.93). Results demonstrate that aircraft-based methane sensing has matured since previous studies and is ready for an increasingly important role in environmental policy and regulation.

Coastal terrestrial-aquatic interfaces (TAIs) are crucial contributors to global biogeochemical cycles and carbon exchange. The soil carbon dioxide (CO2) efflux in these transition zones is however poorly understood due to the high spatiotemporal dynamics of TAIs, as various sub-ecosystems in this region are compressed and expanded by complex influences of tides, changes in river levels, climate, and land use. We focus on the Chesapeake Bay region to (i) investigate the spatial heterogeneity of the coastal ecosystem and identify spatial zones with similar environmental characteristics based on the spatial data layers, including vegetation phenology, climate, landcover, diversity, topography, soil property, and relative tidal elevation; (ii) understand the primary driving factors affecting soil respiration within sub-ecosystems of the coastal ecosystem. Specifically, we employed hierarchical clustering analysis to identify spatial regions with distinct environmental characteristics, followed by the determination of main driving factors using Random Forest regression and SHapley Additive exPlanations. Maximum and minimum temperature are the main drivers common to all sub-ecosystems, while each region also has additional unique major drivers that differentiate them from one another. Precipitation exerts an influence on vegetated lands, while soil pH value holds importance specifically in forested lands. In croplands characterized by high clay content and low sand content, the significant role is attributed to bulk density. Wetlands demonstrate the importance of both elevation and sand content, with clay content being more relevant in non-inundated wetlands than in inundated wetlands. The topographic wetness index significantly contributes to the mixed vegetation areas, including shrub, grass, pasture, and forest. Additionally, our research reveals that dense vegetation land covers and urban/developed areas exhibit distinct soil property drivers. Overall, our research demonstrates an efficient method of employing various open-source remote sensing and GIS datasets to comprehend the spatial variability and soil respiration mechanisms in coastal TAI. There is no one-size-fits-all approach to modeling carbon fluxes released by soil respiration in coastal TAIs, and our study highlights the importance of further research and monitoring practices to improve our understanding of carbon dynamics and promote the sustainable management of coastal TAIs.

With increased interest in forest restoration comes an urgent need to provide accurate, scalable, and cost‐effective monitoring tools. The ubiquity of smartphones has led to a surge in monitoring apps. We reviewed and assessed monitoring apps found through web searches and conversations with practitioners. We identified 42 apps that (1) automatically monitor indicators or (2) facilitate data entry. We selected the five most promising from the first category, based on their relevance, availability, stability, and user support. We compared them to traditional field techniques in a well‐studied restoration project in Costa Rica. We received further feedback from 15 collaborator organizations that evaluated these in their corresponding field restoration sites. Diameter measurements correlated well with traditional tape‐based measurements (R2 = 0.86–0.89). Canopy openness and ground cover showed weaker correlations to densiometer and quadrat cover measurements (R2 = 0.42–0.51). Apps did not improve labor efficiency but do preclude the purchase of specialized field equipment. The apps reviewed here need further development and validation to support monitoring adequately, especially in the tropics. Estimates of development and maintenance costs, as well as statistics on user uptake, are required for cost‐effective development. We recommend a coordinated effort to develop dedicated restoration monitoring apps that can speed up and standardize the collection of indicators and provide evidence on restoration outcomes alongside a centralized repository of this information.

Estimates of the land area occupied by wind energy differ by orders of magnitude due to data scarcity and inconsistent methodology. We developed a method that combines machine learning-based imagery analysis and geographic information systems and examined the land area of 318 wind farms (15,871 turbines) in the U.S. portion of the Western Interconnection. We found that prior land use and human modification in the project area are critical for land-use efficiency and land transformation of wind projects. Projects developed in areas with little human modification have a land-use efficiency of 63.8 ± 8.9 W/m2 (mean ±95% confidence interval) and a land transformation of 0.24 ± 0.07 m2/MWh, while values for projects in areas with high human modification are 447 ± 49.4 W/m2 and 0.05 ± 0.01 m2/MWh, respectively. We show that land resources for wind can be quantified consistently with our replicable method, a method that obviates >99% of the workload using machine learning. To quantify the peripheral impact of a turbine, buffered geometry can be used as a proxy for measuring land resources and metrics when a large enough impact radius is assumed (e.g., >4 times the rotor diameter). Our analysis provides a necessary first step toward regionalized impact assessment and improved comparisons of energy alternatives.

Cyanobacterial harmful algal blooms (cyanoHABs) can harm people, animals, and affect consumptive and recreational use of inland waters. Monitoring cyanoHABs is often limited. However, chlorophyll-a (chl-a) is a common water quality metric and has been shown to have a relationship with cyanobacteria. The World Health Organization (WHO) recently updated their previous 1999 cyanoHAB guidance values (GVs) to be more practical by basing the GVs on chl-a concentration rather than cyanobacterial counts. This creates an opportunity for widespread cyanoHAB monitoring based on chl-a proxies, with satellite remote sensing (SRS) being a potentially powerful tool. We used Sentinel-2 (S2) and Sentinel-3 (S3) to map chl-a and cyanobacteria, respectively, classified chl-a values according to WHO GVs, and then compared them to cyanotoxin advisories issued by the California Department of Water Resources (DWR) at San Luis Reservoir, key infrastructure in Californias water system. We found reasonably high rates of total agreement between advisories by DWR and SRS, however rates of agreement varied for S2 based on algorithm. Total agreement was 83% for S3, and 52%-79% for S2. False positive and false negative rates for S3 were 12% and 23%, respectively. S2 had 12%-80% false positive rate and 0%-38% false negative rate, depending on algorithm. Using SRS-based chl-a GVs as an early indicator for possible exposure advisories and as a trigger for in situ sampling may be effective to improve public health warnings. Implementing SRS for cyanoHAB monitoring could fill temporal data gaps and provide greater spatial information not available from in situ measurements alone.

Recent studies indicate that the Asian Water Tower (AWT) is at risk due to climate change, which can negatively impact water and food security in Asia. However, there is a lack of comprehensive information on lakes' spatial and temporal changes in this region. This information is crucial for understanding the risk magnitude and designing strategies. To fill this research gap, we analyzed 89,480 Landsat images from 1977 ± 2 to 2020 ± 2 to investigate the changes in the size of lakes recharged by the AWT. Our findings showed that out of the 209 lakes larger than 50 km2, 176 (84 %) grew during the wet season and 167 (81 %) during the dry season. 74 % of expanded lakes are located in the Inner Tibetan Plateau (TP) and Tarim basins. The lakes that shrank are found mainly in the Helmand, Indus, and Yangtze basins. Over the entire period, the area of shrinkage (55,077.028 km2 in wet season, 53,986.796 km2 in dry) markedly exceeded expansion (13,000.267 km2 in wet, 11,038.805 km2 in dry), with the drastic decline of the Aral Sea being a major contributor to shrinkage, accounting for 90 % of the total loss. From 1990 ± 2 to 2020 ± 2, alpine lakes mostly expanded, plain lakes mostly shrank, with the opposite trend from 1977 ± 2 to 1990 ± 2. Glacial loss and permafrost thawing under global warming in the Inner TP, Tarim Interior, Syr Darya, and Mekong basins were strongly correlated with lake expansion. However, permafrost discontinuities may prevent significant growth of lakes in the Indus and Ganges basins despite increased recharge. Our findings point to the prominence of the risk the lakes recharged by AWT face. Taking immediate action to manage these risks and adaptation is crucial as the AWT retreats and lake recharges are slowed.

Sun-induced chlorophyll fluorescence (SIF) has proven to be advantageous in estimating gross primary production, despite the lack of a stable relationship. Satellite-based SIF measurements at Level 2 offer comprehensive global coverage and are available in near real time. However, these measurements are often limited by spatial and temporal sparsity, as well as discontinuities. These limitations primarily arise from incomplete satellite trajectories. Additionally, variability in cloud cover and periodic issues specific to the instruments can compromise data quality. Two families of methods have been developed to address data discontinuity: (1) machine learning-based gap-filling techniques and (2) geostatistical techniques (various forms of kriging). The former techniques utilize the relationships between ancillary data and SIF, while the latter usually rely on the available SIF data recordings and their covariance structure to provide estimates at unsampled locations. In this study, we create a synthetic approach for SIF gap filling by hybridizing the two approaches under the umbrella of kriging with external drift. We performed leave-one-out cross-validation of the OCO-2 SIF retrieval aggregates for the entire year of 2019, comparing three methods: ordinary kriging, ML-based estimation using ancillary data, and kriging with external drift. The Mean Absolute Error (MAE) for ML, ordinary kriging, and the hybrid approach was found to be 0.1399, 0.1318, and 0.1183 mW m2 sr−1 nm−1, respectively. We demonstrate that the performance of the hybrid approach exceeds both parent techniques due to the incorporation of information from multiple resources. This use of multiple datasets enriches the hybrid model, making it more robust and accurate in handling the spatio-temporal variability and discontinuity of SIF data. The developed framework is portable and can be applied to SIF retrievals at various resolutions and from various sources (satellites), as well as extended to other satellite-measured variables.

Mountain shrublands are widespread habitats of the European Alps. Shrub encroachment into above treeline grazed lands profoundly modifies biodiversity and ecosystem functioning. Yet, mountain shrublands remain overlooked in vegetation distribution modeling because it is difficult to distinguish them from productive grasslands. Here, we used the pigment-sensitive spectral indices based on Sentinel-2 bands within a specific phenological window, to produce a high-resolution distribution map of mountain shrublands in the French Alps. We evaluated the performance of our classification using a large dataset of vegetation plots and found that our model is highly sensitive to Ericaceous species which constitute most of the dense alpine shrublands in the French Alps. Our analysis of topoclimatic and land use factors limiting the shrubland distribution at regional scale found that, consistent with the ecophysiology of shrubs, expansion is limited by a combination of water deficit and temperature. We discussed the past and current land-use implications in the observed distribution and put forward hypotheses combining climate and land-use trajectories. Our work provides a baseline for monitoring mountain shrub dynamics and exploring the response of shrublands to past and ongoing climate and land use changes.

In recent years, deep learning has significantly reshaped numerous fields and applications, fundamentally altering how we tackle a variety of challenges. Areas such as natural language processing (NLP), computer vision, healthcare, network security, wide-area surveillance, and precision agriculture have leveraged the merits of the deep learning era. Particularly, deep learning has significantly improved the analysis of remote sensing images, with a continuous increase in the number of researchers and contributions to the field. The high impact of deep learning development is complemented by rapid advancements and the availability of data from a variety of sensors, including high-resolution RGB, thermal, LiDAR, and multi-/hyperspectral cameras, as well as emerging sensing platforms such as satellites and aerial vehicles that can be captured by multi-temporal, multi-sensor, and sensing devices with a wider view. This study aims to present an extensive survey that encapsulates widely used deep learning strategies for tackling image classification challenges in remote sensing. It encompasses an exploration of remote sensing imaging platforms, sensor varieties, practical applications, and prospective developments in the field. [ABSTRACT FROM AUTHOR]

• Estimates of modeled CH 4 emission (E) data for 1,325 U.S. landfills were reviewed. • Operators use two methods to estimate E from landfills with a gas collection system. • First-order decay (FOD) method shows increasing emissions. • Collection efficiency assumption method shows decreasing emissions. • FOD method shows some agreement to remotely sensed national estimates. As part of its commitment to the United Nations Framework Convention on Climate Change, the U.S. annually develops a national estimate of methane emissions from municipal solid waste (MSW) landfills by aggregating activity data from each facility. Since 2010, the U.S. has reported a 20 % decrease in MSW landfill emissions despite a 21 % increase in tons disposed. Operator-submitted data were investigated to understand the causes of this decline. In the U.S., operators of landfills with a gas collection and control system (GCCS) calculate their facility's emissions via two separate approaches – (1) first-order decay (FOD) and (2) collection efficiency assumption (CEA) − and select either result to feed into the annual inventory. The FOD model predicts methane generation proportional to waste disposal and that approach calculated a 19 % increase in total methane generated from 2010 to 2022, whereas generation via the CEA approach decreased by 8.9 %. The amount of measured methane collected has increased 7.5 % for the same years. Discrepancies between the two models' generated methane, assumed gas collection efficiencies, and oxidized methane compound into substantive differences in national estimates. Operators more frequently select the CEA method, which results in decreased national estimates. If only the FOD method was used, U.S. MSW landfill emissions would be 1.3-1.7 times greater than current estimates which is similar to recent extrapolations from remote sensing campaigns in the U.S. Both models contain parameters with large inherent uncertainty. Without measurement methods that continuously quantify both point-source and diffuse emissions, an assessment of either equation's accuracy cannot be made. [ABSTRACT FROM AUTHOR]

Damage to the environment or the ecosystem because of natural lightning or human-induced circumstances can lead to losses that reverberate across decades. Harnessing satellite images can be an imperative change that can help avoid relentless and escalating fires. Continuous and comprehensive coverage by remote sensing enables the detection of small plumes of fire or smoke that can emerge as wildfire if not detected early. Mainly, research has been focused on only limited predefined classes. To overcome this limitation the dataset provided by the Moderate Resolution Imaging Spectroradiometer (MODIS), referred as USTC_SmokeRS is utilized in this study. This dataset includes smoke and situations that resemble smoke, such as clouds, dust, and haze. USTC- SmokeRS dataset helps in developing a system that can adapt across diverse geographical regions. This article employs three different pre-trained models: MobilenetV2, InceptionV3, and DenseNet201, which are optimized and are used for the smoke classification task. Along with optimization of the models, an ensemble algorithm is also proposed to combine the capabilities of the three individual models. The proposed ensemble model achieved the highest accuracy of 95.73% on this dataset. Further, the Grad-CAM technique is adapted for localization task. [ABSTRACT FROM AUTHOR]

Climate change threatens current crops, necessitating the development of stress-tolerant alternatives. De novo domestication of already tolerant plants is a promising solution, but the wild progenitors may become weeds that are challenging to identify in the field. Genome editing could add signature traits to de novo domesticated crops to make them recognizable by remote sensing. Genes associated with the biosynthesis of anthocyanin and carotenoid and with leaf and seed morphology are potential editing targets for improving automated recognition of weeds in agriculture. Most high-yielding crops are susceptible to abiotic and biotic stresses, making them particularly vulnerable to the potential effects of climate change. A possible alternative is to accelerate the domestication of wild plants that are already tolerant to harsh conditions and to increase their yields by methods such as gene editing. We foresee that crops' wild progenitors could potentially compete with the resulting de novo domesticated plants, reducing yields. To improve the recognition of weeds, we propose using gene editing techniques to introduce traits into de novo domesticated crops that will allow for visual recognition of the crops by weeding robots that have been trained by machine learning. [ABSTRACT FROM AUTHOR]

In recent years, limited surface space and restricted urban expansion have become increasingly prominent. The development of underground spaces has emerged as an effective solution, offering a new dimension for urban growth and sustainability. However, due to the non-renewable and costly nature of underground space (UGS), it should be properly planned before development to avoid waste of resources and economic loss. In this study, the potential of UGS development based on the influence of surface buildings was evaluated by using the high-resolution satellite images of Gaofen-2 (GF-2) in Qingdao, Shandong Province, China. Firstly, the DeepLabv3+ network was lightened to enhance the training efficiency and building extraction accuracy. The building shadows were then extracted by an improved shadow measurement model, and used to estimate the height of extracted buildings based on the imaging mechanism and information of the remote sensing image. Finally, the building height (BH) was converted into the depth of influence on the UGS, and the development potential of the UGS was then estimated. The experimental results show that the UGS within 0–10 m in Qingdao city has been extensively occupied. However, the geological layers between 10–30 m and 30–50 m contain large continuous areas that have not been utilized, indicating the greatest potential for resource development within the city. [ABSTRACT FROM AUTHOR]

Cotton is one of the crops that requires the most time and labor. Precision agriculture technology is required for efficient management of cotton, and the identification of cotton attribute information in the field is a necessary and crucial step towards implementing precision agriculture. Unmanned aerial vehicles (UAVs) and Light Detection and Ranging (LiDAR) have evolved into essential instruments for plant phenotyping research. In this study, in order to address the demand for cotton attribute identification over wide areas in the field, an airborne LiDAR system was built based on LiDAR detection technology. This work acquired a dual-view point cloud of a cotton field in order to address the high density and low accuracy of the cotton point cloud attributes. Following pre-processing of the data, the point cloud was first coarsely regenerated using a combination of Fast Point Feature Histograms (FPFH) and Intrinsic Shape Signatures (ISS) techniques. The dual-view point cloud registration was then refined and finished using an Iterative Closest Point (ICP) algorithm. The height of the cotton plant was determined using the reconstructed point cloud of the cotton canopy, and a method combining Graham's algorithm and the Alpha-Shape algorithm was suggested to determine the porosity of the cotton layers. The findings revealed that the root mean square errors (RMSE) between calculated and measured values of cotton plant height and stratified porosity were, respectively, 3.98 cm and 5.21%, and that their mean absolute percentage errors (MAPE) were 4.39% and 9.31%, with correlation coefficients (${R^2}$ R 2 ) of 0.951 and 0.762, respectively. On the whole, our study has demonstrated the effectiveness of the proposed method in terms of providing accurate and reliable cotton parameters in agriculture. [ABSTRACT FROM AUTHOR]

Pansharpening is an important technology for obtaining high-resolution multispectral (HRMS) images by fusing low-resolution multispectral (LRMS) images and high-resolution panchromatic (PAN) images. Although many pansharpening models have emerged by taking advantage of deep learning (DL) technology, there remains a pressing need to further assess pansharpening accuracy and stability when LRMS images with complex land-cover types. What's more, these models often overlook the exploitation of PAN images' inherent high-frequency information. To address these issues, we propose a pansharpening model combining multi-level and multi-scale network architectures. The multi-level network architecture is used to build spatial-spectral dependence on LRMS-PAN pairs, and strengthen the network's feature capture capability by keeping the multi-level texture details. The multi-scale architecture is subsequently used to extract the spatial structure and deep texture of the PAN images at different scales. Downsampled experiments and real experiments in four standard datasets show that the proposed model achieves a state-of-the-art performance. [ABSTRACT FROM AUTHOR]

A large number of objects of different scales appear aggregative distribution in remote sensing images, which brings two challenges to remote sensing object detection. The first challenge arises from the presence of objects with large scale differences within the same scene, which complicates the detector's ability to balance the detection performance between large and small objects. The second challenge is that the distribution of dense objects on remote sensing images with complex backgrounds can reduce the feature representation capability of the network, leading to false detections and missed detections of objects. To address the two challenges present in remote sensing object detection, we propose a new object detection network DA-GLANet (Direction-Aware Multi-Branch Attention and Gaussian Label Assignment Balance Net). For the first challenge, we designed a multi-scale feature fusion module to fully utilize semantic information at different scales, thereby improving the detection accuracy of multi-scale objects. Subsequently, to tackle the second challenge, we proposed a multi-branch attention module with direction-aware capabilities. This module can enhance the network's feature representation of objects of interest by generating attention maps that are both direction-aware and position-sensitive. In addition, we propose a label assignment strategy based on Gaussian distribution to address the imbalance of positive and negative labels caused by the complexity of remote sensing images. These methods work synergistically to improve aggregative object detection accuracy. We have conducted extensive experiments on the DOTA dataset, Levir dataset, and WHU buildings change detection dataset to demonstrate the superiority of our method. On the DOTA dataset, our method improves mAP by 0.4%, 0.3%, and 0.2% compared to the top three methods. On the Levir dataset, our method improves mAP by 2.5%, 2.2%, and 1.6% compared to the top three methods. [ABSTRACT FROM AUTHOR]

Small vessels contribute substantially to coastal soundscapes, presenting myriad potential impacts on marine species and ecosystems, yet are challenging to monitor using standard tracking methods (e.g. AIS). Here, we present an automated camera system developed to quantify coastal vessel traffic in a core habitat for Hector's dolphins (Cephalorhynchus hectori) within Akaroa Harbour, Aotearoa New Zealand. Analysis of 55,569 images taken during the austral summer of 2019/20 showed that traffic was highest around midday, on weekends, and in January. A mean of 41.9 vessel trips per day transited the area of core habitat. Vessel speeds estimated from photographs were assigned to 5-knot bins with 84% accuracy. Findings suggest vessel traffic in Akaroa Harbour has approximately doubled between 2006–2008 and 2020. Additionally, tours focused specifically on dolphins composed a significantly larger proportion of vessel traffic in 2020 than observed during 2006–2008. We validate the use of an automated camera system as a robust, novel, repeatable, and cost-effective tool for quantifying fluctuations in coastal vessel traffic. Such tools can provide baseline data and help assess impacts, and their use can guide management action. [ABSTRACT FROM AUTHOR]

As the global climate continues to change, plants will increasingly experience abiotic stress(es). Stomata on leaf surfaces are the gatekeepers to plant interiors, regulating gaseous exchanges that are crucial for both photosynthesis and outward water release. To optimise future crop productivity, accurate modelling of how stomata govern plant–environment interactions will be crucial. Here, we synergise optical and thermal imaging data to improve modelled transpiration estimates during water and/or nutrient stress (where leaf N is reduced). By utilising hyperspectral data and partial least squares regression analysis of six plant traits and fluxes in wheat (Triticum aestivum), we develop a new spectral vegetation index; the Combined Nitrogen and Drought Index (CNDI), which can be used to detect both water stress and/or nitrogen deficiency. Upon full stomatal closure during drought, CNDI shows a strong relationship with leaf water content (r2 = 0.70), with confounding changes in leaf biochemistry. By incorporating CNDI transformed with a sigmoid function into thermal‐based transpiration modelling, we have increased the accuracy of modelling water fluxes during abiotic stress. These findings demonstrate the potential of using combined optical and thermal remote sensing‐based modelling approaches to dynamically model water fluxes to improve both agricultural water usage and yields. Summary statement: Using both trait and flux measurements, we have designed a new vegetation index: the combined nitrogen and drought index. This index describes changes in leaf optical properties during nitrogen and or drought stress that can be used to improve transpiration modelling during abiotic stress. [ABSTRACT FROM AUTHOR]

Accurate landslide segmentation from remote sensing data is pivotal for efficient emergency response and risk management. In recent years, data-driven deep learning approaches have emerged as a significant area of focus in this domain. However, the limited availability of landslide data often restricts the effectiveness of these approaches. This study introduces the StyleGAN2-transformer framework for landslide segmentation, utilizing generative adversarial networks (GANs) for the first time to create synthetic, high-quality landslide images to address the data scarcity issue that undermines landslide segmentation model performance. Two datasets were developed: one containing a limited set of real landslide images and the other supplemented with synthetic landslide images generated by StyleGAN2. These datasets facilitated comparative experiments to quantitatively assess the impact of synthetic data on the performance of both convolutional neural network (CNN) and transformer series models, employing a suite of metrics for thorough evaluation. The findings indicate that adding synthetic landslide images from StyleGAN2 improves the overall accuracy of most landslide segmentation models significantly, achieving more than a 10% increase. Moreover, integrating StyleGAN2 with transformer models presents an optimized approach, as transformer models surpass CNN models in accuracy when adequate training data are available. Finally, the results also confirm that the StyleGAN2-transformer framework exhibits strong generalizability in a variety of scenarios. [ABSTRACT FROM AUTHOR]

Satellites engaged in observation missions often face severe pointing constraints, which can limit the ability of their solar panels optimize the generation of electrical power during payload operations. Some approaches to maximize power generation involve either using orientable solar panels, or performing orbital maneuvers to point fixed or deployable (non-orientable) solar panels towards the Sun, which can compromise the payload's ability to meet its pointing requirements. In this study, an alternative approach that enables high-consuming remote sensing payloads to operate for extended periods without requiring orientable solar panels is presented. The satellite under consideration is equipped with fixed or deployable (non-orientable) solar panels mounted on up to three different perpendicular faces. The on-board payload is required to remain within a fixed angle from a target direction, which can be either nadir or a specific geographical position. To derive an optimal tracking law, the projected solar array area at each instant is maximized, resulting in optimal electrical power generation. This methodology allows the satellite to maintain its pointing constraints and efficiently operate high-consuming payloads for longer periods. The proposed tracking law is validated using an actual mission scenario, comparing the power generation achieved by the proposed method against other techniques. The approach presented in this paper shows improved performance, enabling satellites equipped with non-orientable solar panels to fulfill their observation requirements while optimizing electrical power generation. This work offers significant benefits for satellite operators, reducing the need for costly orientable solar panels and enhancing the overall efficiency of satellite missions. • Guidance law for optimal power generation while complying with payload pointing constraints. • Simple and easy formulation, suitable for real-time systems. • Methodology validated and compared against other attitude strategies. • Integration and simulation of the guidance law into a control scheme. [ABSTRACT FROM AUTHOR]

Automated above-water hyperspectral observations are often subject to inaccuracies caused by instrument malfunction and environmental conditions. This study evaluates the influence of atmospheric and water surface conditions on above-water hyperspectral measurements through statistical methods and Radiative Transfer (RT) modelling. Initially, we developed a general quality control method based on statistical assessment to detect the suspicious spectra. Subsequently, Radiative Transfer (RT) models were used to assess low light conditions, distortions in the spectral shape of above-water solar downwelling irradiance (E S (λ), mW m−2 nm−1) particularly those caused by intense atmospheric scattering and/or reddish hue of dusk or dawn radiation, the effect of atmospheric humidity and precipitation on the intensity and shape of spectra, and the influence of sun glint and surface perturbations on sky (L S (λ), mW m−2 nm−1 sr−1) and water surface (L T (λ), mW m−2 nm−1 sr−1) radiances. The proposed methods were applied to the entire archive of automated above-water hyperspectral measurements collected every ten minutes from 2020 to 2022 at the Royal Netherland Institute for Sea Research (NIOZ) at Jetty Station (NJS) located in the Marsdiep tidal inlet of the Duch Wadden Sea, the Netherlands. The findings demonstrate that low light conditions are characterized by E S (λ) max ≤ 25 mW m−2 nm−1. Red-shifted or distorted spectra are indicated by a ratio of E S (4 8 0)/ E S (6 8 0) ≤ 1.0 and E S (λ max)/ E S (8 6 5) ≤ 1.25. High humidity/precipitation conditions are identified by the ratio of E S (9 4 0)/ E S (8 6 5), which varies with the Solar Zenith Angle (SZA). Furthermore, significant sun glint and surface perturbations, such as whitecaps and foam, are indicated when the minimum ratio of L T (800 nm-950 nm)/ E S (800 nm-950 nm) > 0.025 sr−1, and the ratio of L T (850 nm)/ E S (850 nm) ≥ 0.025 sr−1. [ABSTRACT FROM AUTHOR]

[Display omitted] Evapotranspiration (ET), which represents water loss due to soil evaporation and crop transpiration, is a critical hydrological parameter for managing available water resources in irrigation systems. Traditional methods for monitoring actual evapotranspiration (ETa) involve field measurements. While accurate, they lack scalability, are labor-intensive, and incur high costs. Remote sensing satellites can help address these practical challenges by providing high-resolution imagery for spatially explicit mapping and near-real-time monitoring of ETa. This study aimed to develop simple yet robust models for estimating ETa using Sentinel-2 (S2A and S2B) satellite vegetation indices (VIs)—the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI)—and the Google Earth Engine (GEE) cloud platform for irrigated sugarcane plantations of the Metehara Sugarcane Estate in the semiarid landscape of the Ethiopian Rift Valley. Six empirical ET-VI models that combined NDVI-based proxies (NDVI Kc , NDVI*, and NDVI* scaled) and EVI-based proxies (EVI Kc , EVI*, and EVI* scaled) for the crop coefficient (Kc) with the reference ET (ETo) were developed and evaluated for growing seasons between 2020 and 2022. Model validation using independently estimated sugarcane ET (ET sugarcane) and open-access remote sensing ET, Actual EvapoTranspiration and Interception (ETIa) showed that all ET-VI models captured spatiotemporal dynamics in the consumptive fraction of sugarcane water use, with a higher coefficient of determination (R2) of ≥ 0.91. However, comparative analyses of ETa retrieval models indicated improved accuracy of the ET-EVI models (root mean square error (RMSE) of ± 8 mm for ET sugarcane and ± 4 mm for ETIa) compared with the ET-NDVI models. Among the EVI models, ET-EVI Kc achieved the highest R2 of 0.98, RMSE of ≤ 30 mm, and percentage bias (PBIAS) of ≤ 15 %. The results also revealed a strong correlation between the scaled VI-derived models and the reference ETIa (R2 = 0.94–0.97), which best explained the field-by-field variability, with the ET-EVI* scaled model achieving a lower RMSE of 18 mm than the ET-NDVI* scaled model (RMSE= 32 mm), while both the models showed similar levels of bias (∼17 %). Moreover, compared to the referenced ET sugarcane , the bias was minimal at − 9 % for ET-NDVI* scaled and − 1 % for ET-EVI* scaled. At the field scale, the NDVI and EVI models estimated the mean monthly ETa ranging from 99 to 129 mm m−1 and 89 to 148 mm m−1, respectively, with total annual averages of 1188–1537 mm yr−1 and 1296–1566 mm yr−1. In this context, the modeled ETa provided improved insights into consumptive water use in irrigated sugarcane plantations with limited field measurements. The statistical model evaluation metrics indicated that ET-EVI Kc was the optimal model in characterizing ET sugarcane , outperforming the ET-NDVI Kc and ET-EVI* scaled models, which ranked second by > 6 %, and ET-NDVI* scaled model , which ranked third by > 20 %. Our findings demonstrate the potential of multispectral VI-driven models as cost-effective and practical tools for the rapid estimation and mapping of ETa, thereby supporting the development of sustainable water conservation practices. A major advantage of the empirical modeling framework presented in this study is the straightforward parametrization of spatially consistent Kc distributions using remote sensing VIs and local weather station data. However, further improvements and operational applications of standardized VI-based ET models in croplands of other large irrigation schemes in semiarid regions should consider atmospheric impacts, variations in scene characteristics, and bare ground/soil exposure. [ABSTRACT FROM AUTHOR]

The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) is an active spaceborne remote sensing system that utilizes photon-counting LiDAR to capture highly detailed information about under-vegetation terrain and forest structure over vast spatial regions. It facilitates the accurate retrieval of terrain elevation and canopy height information, critical for assessing the global carbon budget and understanding the role of forests in climate change mitigation. However, challenges arise from the characteristics of the ICESat-2 photon-counting LiDAR data, such as their linear distribution, extensive spatial coverage, and substantial residual noise. These challenges hinder the performances of the state-of-the-art methods when applied on ICESat-2 data for extracting ground or top of canopy, while they perform well on airborne LiDAR that is featured with planar distribution, small coverage, and high signal-to-noise ratio. Consequently, this study proposes a novel algorithm termed Adaptive Linear Cloth Simulation Filtering (ALCSF), for the automated extraction of ground and top-of-canopy photons from ICESat-2 signal photons. The ALCSF algorithm innovatively introduces a cloth strip model as a reference to accommodate the distribution characteristics of ICESat-2 photons. Additionally, it employs a terrain-adaptive strategy to adjust the rigidity of the cloth strip by utilizing terrain slope information, thus making ALCSF applicable to large-scale areas with significant topographical changes. Furthermore, the proposed ALCSF addresses noise interference by simultaneously considering the movability of particles of the cloth strip model and the photon distribution during iterative adjustments of the cloth strip. The performance of the ALCSF is evaluated by comparing it with the ICESat-2 Land–Vegetation Along-Track Products (ATL08) across twelve datasets that encompass various times of day and scenes. In the results, the ALCSF exhibits notable improvements over ATL08 products, effectively reducing the root mean square error (RMSE) of ground elevation by 21.8% and canopy height by 25.8%, with superior performance in preserving terrain details. This highlights the significance of ALCSF as a valuable tool for enhancing the accuracy of ICESat-2 land and vegetation products, ultimately contributing to the estimation of the global carbon budget in future studies. [ABSTRACT FROM AUTHOR]

Change detection in high-resolution Earth observation is a fundamental Earth vision task to understand the subtle temporal dynamics of Earth's surface, significantly promoted by generic vision technologies in recent years. Vision Transformer is a powerful component to learning spatiotemporal representation but with enormous computation complexity, especially for high-resolution images. Besides, there is still lacking principles in designing macro architectures integrating these advanced vision components for various change detection tasks. In this paper, we present a deep probabilistic change model (DPCM) to provide a unified, interpretable, modular probabilistic change process modeling to address multiple change detection tasks, including binary change detection, one-to-many semantic change detection, and many-to-many semantic change detection. DPCM describes any complex change process as a probabilistic graphical model to provide theoretical evidence for macro architecture design and generic change detection task modeling. We refer to this probabilistic graphical model as the probabilistic change model (PCM), where DPCM is the PCM parameterized by deep neural networks. For parameterization, the PCM is factorized into many easy-to-solve distributions based on task-specific assumptions, and then we can use deep neural modules to parameterize these distributions to solve the change detection problem uniformly. In this way, DPCM has both theoretical macro architecture from PCM and strong representation capability of deep networks. We also present the sparse change Transformer for better parameterization. Inspired by domain knowledge, i.e., the sparsity of change and the local correlation of change, the sparse change Transformer computes self-attention within change regions to model spatiotemporal correlations, which has a quadratic computational complexity of the change region size but independent of image size, significantly reducing computation overhead for high-resolution image change detection. We refer to this instance of DPCM with sparse change Transformer as ChangeSparse to demonstrate their effectiveness. The experiments confirm ChangeSparse's superiority in speed and accuracy for multiple real-world application scenarios, such as disaster response and urban development monitoring. The code is available at https://github.com/Z-Zheng/pytorch-change-models. More resources can be found in http://rsidea.whu.edu.cn/resource_sharing.htm. [ABSTRACT FROM AUTHOR]

Laser scanning is an active remote sensing technique applied in many disciplines to acquire state-of-the-art spatial measurements. Semantic labeling is often necessary to extract information from the raw point cloud. Deep learning methods constitute a data-hungry solution for the semantic segmentation of point clouds. In this work, we investigate the use of simulated laser scanning for training deep learning models, which are applied to real data subsequently. We show that training a deep learning model purely on virtual laser scanning data can produce results comparable to models trained on real data when evaluated on real data. For leaf-wood segmentation of trees, using the KPConv model trained with virtual data achieves 93.7% overall accuracy, while the model trained on real data reaches 94.7% overall accuracy. In urban contexts, a KPConv model trained on virtual data achieves 74.1% overall accuracy on real validation data, while the model trained on real data achieves 82.4%. Our models outperform the state-of-the-art model FSCT in terms of generalization to unseen real data as well as a baseline model trained on points randomly sampled from the tree mesh surface. From our results, we conclude that the combination of laser scanning simulation and deep learning is a cost-effective alternative to real data acquisition and manual labeling in the domain of geospatial point cloud analysis. The strengths of this approach are that (a) a large amount of diverse laser scanning training data can be generated quickly and without the need for expensive equipment, (b) the simulation configurations can be adapted so that the virtual training data have similar characteristics to the targeted real data, and (c) the whole workflow can be automated through procedural scene generation. [Display omitted] • Virtual laser scanning generates data suitable for training deep learning models. • Deep learning models trained with simulated 3D data generalize to real data. • More detailed scene modeling leads to improved virtual laser scanning-based training. • Automatic model training through procedural scene generation and laser simulation. [ABSTRACT FROM AUTHOR]

In remote sensing (RS) image change detection (CD) task, many existing CD methods focus more on how to improve accuracy, but they usually have more parameters, higher computational costs, and heavier memory usage. Designing lightweight and performance-sustainable CD model that is more compatible with real-world applications is an urgent problem to be solved. Therefore, we propose a lightweight change detection network, called as robust feature aggregation network (RFANet). To improve representative capability of weaker features extracted from lightweight backbone, a feature reinforcement module (FRM) is proposed. FRM allows current level feature to densely interact and fuse with other level features, thus accomplishing the complementarity of fine-grained details and semantic information. Considering massive objects with rich correlations in RS images, we design semantic split-aggregation module (SSAM) to better capture global semantic information of changed objects. Besides, we present a lightweight decoder containing channel interaction module (CIM), which allows multi-level refined difference features to emphasize changed areas and suppress background and pseudo-changes. Extensive experiments carried out on four challenging RS image CD datasets illustrate that RFANet achieves competitive performance with fewer parameters and lower computational costs. The source code is available at https://github.com/Youzhihui/RFANet. [ABSTRACT FROM AUTHOR]

This paper takes the collapse along the transmission corridor of The Deep - Cut Gorge Area of Dadu River in Erlangshan - Zheduo Mountain as the research object. The paper researches the characteristics of the development of the collapse disaster and the factors of the disaster. It is based on the remote sensing interpretation and the geological disaster investigation in the area. The results show that a total of 42 collapses were developed in the study area. The main controlling factors for the collapses in the area are topography, slope direction, geological structure, rock formation, annual rainfall, etc. The collapses were mainly developed in the area of rigid or relatively rigid rocks, and the rainfall was measured between 834-965 mm. There were 22 collapses in this area. The influence of the current collapse development along the transmission corridor in the Deep - Cut Gorge area of the Erlang Mountain - Zheduo Mountain Dadu River on the power transmission project is mainly to threaten the stability of the tower and its foundation. The research results provide a theoretical basis for the evaluation and prevention of geological disaster along the power transmission corridor. In addition, the results provide technical guidance for selecting lines, as well as for designing and constructing power transmission projects. [ABSTRACT FROM AUTHOR]

To reveal the evolution characteristics and driving factors of net primary productivity (NPP) of vegetation in arid regions, with Kashi region as a representative case, this study utilized various remote sensing satellite data, including MOD13A1/Q1, MOD09A1, and MOD17A3, to estimate vegetation NPP in the study area from 2001 to 2020 using the CASA model. The study aimed to express the impact of external driving factors on vegetation NPP from the perspectives of climate change and land use change. The results indicated: (1) Over the past two decades, the overall trend of vegetation NPP in the study area exhibited an initial increase, followed by a subsequent decrease. Specifically, from 2001 to 2015, vegetation NPP increased at a rate of 0.303, while from 2016 to 2020, it decreased at a rate of -0.202. (2) Through trend analysis and significance testing using the Sen+MK method, it was found that 37% of the study area showed a significant increasing trend in NPP, while 73% exhibited a decreasing trend. (3) Precipitation in the Kashi region had a stronger correlation with NPP than temperature. In the southwest region, the Pamir Plateau and Karakoram Mountains showed positive correlations with both temperature and precipitation, with warm and moist airflows promoting NPP in high latitudes. (4) )During the period of 2001-2020, Grassland in the study area degraded at the fastest rate, with a change rate of -2.1%, and the largest increase was observed in farmland, with a change rate of 2.7%; the vegetation NPP decreased by a total of 2748.3 TgC, and the study area shows a decreasing trend in the value of vegetation NPP in the future period. [ABSTRACT FROM AUTHOR]

Similar to numerous water- and data-scarce regions, Egypt confronts a critical challenge in sustaining food production for its rapidly growing population. Consequently, the country's water and land resources are under considerable stress and require careful management. About half of Egypt's both annually harvested areas and renewable freshwater are allocated for cultivating rice, maize, wheat, and berseem clover. However, the extent to which crop production might be improved and how this would impact future water and land requirements remains poorly understood. We analyzed potential improvements in the production of these crops and quantified their future water and land requirements under different scenarios. Potential improvements were detected through percentile analysis in three remote sensing-derived performance indicators for each crop in the Nile Delta's Zankalon region: (i) crop yield, (ii) crop water productivity, and (iii) transpiration fraction (transpiration to actual evapotranspiration, T/AET). We applied detected improvementsto construct plausible scenarios for Egypt's water and land requirements to sustain domestic crop production until 2050. Our findings indicate limited potential to improve T/AET (< 4%). However, improvements of up to 27% for crop yields and up to 14% for water productivity are possible. To meet the production targets by 2050, national production must increase by 128, 78, 69, and 71% above the 2016–2020's average for rice, maize, wheat, and berseem, respectively. Depending on the improvement levels in the developed scenarios, a total harvested land area between 5.3 and 6.4 million ha will be required by 2050, with 18% allocated to rice, 28% to maize, 36% to wheat, and 18% to berseem. Associated freshwater requirements will amount to 59–68 billion cubic meters, divided into 23% for rice, 34% for maize, 28% for wheat, and 15% for berseem. Interventions increasing yields and water productivity will benefit more the summer (rice and maize) than the winter crops (wheat and berseem). We discuss likely interventions for meeting these requirements and for sustaining the supply of these crops in Egypt. [ABSTRACT FROM AUTHOR]

One of the most prominent factors contributing to global landscape cover change is land use and land cover (LULC). Such changes are the outcome of several interrelated factors. This study assessed trends in land use and land cover within Hirpora from 1992 to 2006 and 2021 utilizing remote sensing data and satellite pictures from Landsat 5-TM in 1992, Landsat 7-enhanced TM in 2006, and Landsat 8-OLI in 2021. The images were analyzed using ArcGIS v10.1 and ERDAS Imagine v14. From 1992 to 2021, there was a substantial shift in land use trends in a few targeted classes, with snow cover losing the most (33.76%), followed by dense forests (6.80%), and economically grasslands (21.46%), barren/rocky (11.43%), and scrub (5.41%) gaining the most. During the observation period, moderately dense forests and build-up increased in area by 4.50 and 0.35%, respectively. Due to large-scale human intrusion and habitat fragmentation, the Hirpora Wildlife Sanctuary has swiftly transitioned from a natural to a cultural landscape. As part of the LULC change process, natural, demographic, and economic factors have impacted land and had environmental implications. [ABSTRACT FROM AUTHOR]

Landslides are among the most dangerous and catastrophic natural hazards with countless concerns. In disaster rescue operations, fast and precise identification of landslides is necessary for timely and effective preventive actions. The landslide risk is anticipated to be reduced through their prediction, monitoring, and accurate detection using remote sensing technology. Moreover, deep learning algorithms have shown excellent improvement in various remote sensing applications. Recent scientific and intelligent technological innovations are needed to be applied to disaster management and assessment, particularly landslides. Therefore, this study aims to extract the landslide hazard information from multiple data sources, i.e., satellite and unmanned aerial vehicle (UAV) images, using a single staged object detection model, i.e., YOLOv5, YOLOv6, YOLOv7, and YOLOv8. The data from distinct platforms are utilized to infer the synergies between them. The results of each database are evaluated quantitatively using standard methods, i.e., precision, recall, f-score, and mean average precision, whereas visual analysis of results is conducted for qualitative assessment. Based on the experimental results, the highest f-score is represented by YOLOv7 (0.995) and YOLOv5 (0.921) for satellite and UAV-based data, respectively. The quantitative results are further compared with previous research work to exhibit the novelty and competence of the proposed research. Our work demonstrates the application and feasibility of the YOLO model in landslide information extraction for quick hazard recovery operations. [ABSTRACT FROM AUTHOR]

In this paper, ground deformation measurements from synthetic aperture radar interferometry (InSAR) and ground-based prism monitoring are compared to finite-element (FE) simulation results for a recent tailings dam collapse. The InSAR monitoring demonstrated the complex spatial and temporal variability of the tailings dam deformation that is not captured in traditional, point-based monitoring approaches. Potentially anomalous deformation behaviour could have been detected from InSAR 1 year preceding the dam failure. A two-dimensional coupled-consolidation analysis was used to predict the dam behaviour during construction stages. The FE modelling results were in broad agreement with the deformation measurement sources, both in terms of magnitude and trends preceding failure. The results, however, deviate significantly immediately before failure, following the construction of the buttresses. Moreover, the FE modelling is sensitive to parameter uncertainties, such as spatial variability of foundation soil properties. The FE results revealed that deformation in both the upstream and downstream parts of the dam are sensitive to changing foundation properties. However, this impact is potentially much more significant on the downstream compared to the upstream parts of the dam, which are dominated by the behaviour of the tailings. This study highlights the efficacy and complementarity of geotechnical and remote sensing techniques for the monitoring of tailings dams. [ABSTRACT FROM AUTHOR]

In recent years, the frequency of floods has escalated due to global warming and human activities. Addressing this challenge, our study investigates how future climate change scenarios will affect flood susceptibility in the Tajan watershed, northern Iran. The primary objective is to quantify and map the evolving risk of flooding in this region under different future climate scenarios. We applied machine learning techniques, coupled model intercomparison project phase 6 (CMIP6) climatic models, and remote sensing to achieve this goal. The CanESM5 climate model was chosen for its accuracy among four global climate models in CMIP6 to estimate future precipitation trends under shared socioeconomic pathways (SSP 2.6, 4.5, and 8.5) over two-time horizons: future (2020–2060) and far future (2061–2100). These scenarios encompass various influential factors, such as greenhouse gas emissions, urbanization, deforestation, and socioeconomic development, which played crucial roles in modulating flood susceptibility. Flood susceptibility maps were generated considering future precipitation patterns and scenarios using random forest (RF) and support vector machine (SVM) algorithms, 432 flood locations, and 15 flood influencing factors. The accuracy of our prediction was validated through multiple statistical measures, including the area under the receiver operating characteristic (AUC-ROC) curve. The results indicated that the proposed models performed well, with the RF model (AUC=0.91) demonstrating higher accuracy compared to the SVM model (AUC=0.85). From a spatial perspective, increased future precipitation under all SSP scenarios enhances the likelihood of flood occurrences in the central and downstream regions. In the far future, intensified precipitation due to changes in regional topography and climate, coupled with higher greenhouse gas concentrations, is expected to heighten flood risks, especially at higher altitudes. We hope that our study findings will inform effective flood risk management strategies and adaptation plans in response to climate-induced flood risks, both in our study area and in similar regions globally. [ABSTRACT FROM AUTHOR]

• Improving Coastline Edge Detail: EDS module enhances edge fitting. • Bridging Semantic Gap: CSDS and AFM collaboration enhances semantic information. • Outperforms with mIoU: CSAFNet achieves remarkable 96.72% mIoU value. The extraction of coastlines from remote sensing images is vital for promoting sustainable development in coastal areas, conserving marine environments, strengthening disaster response capabilities, and supporting scientific research. However, current coastline detection approaches using remote sensing face challenges related to resolution, terrain, boundary, and data, requiring accurate solutions for reliability. Here, we introduce the Collaborative Supervision and Attention Fusion (CSAFNet) model for pixel-level sea-land segmentation, with a primary goal of improving the accuracy of coastline extraction. The model integrates the Edge Deep Supervision (EDS) module to enhance coastline edge detail fitting. Additionally, the Collaborative Semantic Deep Supervision (CSDS) module and Attention Fusion Module (AFM) collaborate to bridge the semantic gap between different hierarchical features, resulting in a more precise and detailed delineation of coastlines. Experimental validation on the publicly available SLSD 1 1 The dataset was initially labeled as "sea-land segmentation data" by the authors. For ease of discussion, we will adopt the practice of using the initial letters of each word as an abbreviation. dataset has demonstrated superiority over various advanced methods, with an impressive mIoU value of 96.72%. Through simple optimization, detailed and rich coastlines can be extracted, validating the feasibility of coastline extraction. [ABSTRACT FROM AUTHOR]

• Landsat bands alone may not give adequate quality of LULC mapping in a heterogeneous landscape. • Remote sensing-derived indices and auxiliary data improve the accuracy of LULC mapping. • LULC maps classified under different classification algorithms have shown a significant discrepancy. Land use/land cover (LULC) information is regarded as one of the most important variables in global change studies. Several LULC classification algorithms are available with different levels of accuracy in LULC mapping under different geographical setups. Furthermore, different remote sensing (RS) based indices and auxiliary data have been reported to have contributed to the accuracy of LULC mapping. Hence, the main aim of this study was to compare the different ML algorithms for the LULC mapping and explicitly assess the potential contribution of different RS-derived indices and auxiliary data on classification accuracy in the Upper Tekeze-Atbarah (UTA) river basin in Ethiopia. The overall classifiers evaluation result revealed that random forest (RF) performed best and followed by K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), Maximum Likelihood Classification (MLC), and Classification and Regression Tree (CART). Compared to RF, the MLC method showed less accuracy by 16.2% (19.7%) in OA (kappa coefficient) respectively, which showed that the currently emerging ML classifiers contributed to an improvement in LULC mapping. The use of RS-derived indices and DEM as predictors together with the Landsat bands improved the overall accuracy and kappa coefficient by 5.5% and 7% respectively. Furthermore, the discrepancy in the classified LULC map from RF and other classifiers was compared at pixel-level and the result showed a considerable disagreement between the LULC maps. For instance, when the LULC map from RF and KNN are compared, only 80.6% of the area has the same LULC condition. This result reflects the potential discrepancy that could exist in LULC maps from different classifiers. The hydrological simulation under each of the classified map also resulted in a considerable difference in the major water balance components. In a conclusion, given the considerable difference in classifier performance and the potential propagation of errors in LULC maps to further applications such as water resources assessment, it is highly recommended to explicitly evaluate the available classifiers. Users should also harness the power of RS indices and auxiliary datasets for improved LULC mapping. [ABSTRACT FROM AUTHOR]

• The DTFA method showed higher extraction and classification accuracy compared to the SVM and k-means++ methods. • HY-1C has the most effective extraction, followed by Landsat 8. • Landsat 8 is able to extract new ice, grey ice, and grey-white ice with the best effect, while HY-1C has a better effect in extracting white ice. MODIS data has advantages in coverage and data updating. This paper presents the extraction and classification of sea ice in the Yellow and Bohai seas using multi-source remote sensing products, including Landsat 8, HY-1C, and MODIS. We propose a Dynamic Thresholding Fully Automated (DTFA) method for sea ice extraction and classification. The accuracy of sea ice extraction and classification is evaluated using four metrics: confusion matrix accuracy rate, overall accuracy, user accuracy, and kappa coefficient. The DTFA method showed higher extraction and classification accuracy compared to the SVM and k-means++ methods. HY-1C has the most effective extraction, followed by Landsat 8. Landsat 8 is able to extract new ice, grey ice, and grey-white ice with the best effect, while HY-1C has a better effect in extracting white ice. MODIS data has advantages in coverage and data updating. Finally, the fusion of multi-source remote sensing images was used to obtain the sea ice extent of the entire Chinese offshore. This method is also applicable to remote sensing monitoring of sea ice in other sea areas. [ABSTRACT FROM AUTHOR]

• Identified the severity and frequency of drought in Singbhum Protocontinent, India. • Meteorological and remote sensing indices are used to identify drought dynamics. • 1.67 to 1.05 percent extreme drought frequency shows in the western part. • Poor aquifer properties and extreme climate are the main reason for droughts. • Sustainable watershed management is highly required. Drought is an extremely hazardous situation that predominantly affects ecosystems, hydrological systems, and agricultural mechanisms in different pockets of the world. Management of water resources in dry and semi-arid environments has become a critical challenge in the last few decades. The principal objectives of this study are (i) to categorize the severity and frequency of drought events using the Reconnaissance Drought Index and Rainfall Deviation Index and ii) to evaluate the spatial extension of drought in particular drought times of severe and extreme droughts through Remote Sensing Drought Indices (RSDIs). This drought monitoring index is commonly used index for its high degree of resilience and sensitivity to droughts. The result shows that severe to extreme drought occurred in Bero, Tulin, Sirkabad, Manbazar and Bundwan which are 22, 28, 23, 14 and 25 months correspondingly based on RDI3 whereas RDI12 values indicated 26, 47, 42, 22 and 38 months in the study period (1981 to 2021). Bundwan area faced the most extreme drought occurrence in May month of 1996 with −3.58 values followed by Sirkabad, Bero, Manbazar and Tulin. Manbazar is going through severe to extreme drought months (for short time) whereas 'extreme drought' frequency (1.67 % to 1.05 %) is higher in the western part mainly in Jhalda I, Jhalda II, Bagmundi, and Arsha blocks of Purulia district (part of Manbhum Singhbhum Plateau). Additionally, the recurrence of 'moderate drought' is much greater in Balarampur along with some portions of Purulia I block. The whole study can be summarized as the northeastern portion being challenged with moderate or severe drought conditions while mild or severe drought is a considerable recurrence in the northern section. The outcome of this study will be helpful for local planners and management officials during implementation strategies related to water resource management. [ABSTRACT FROM AUTHOR]

This study analysed long-term shoreline change, the influence of erosion, and adaptation strategies in coastal Ghana. The change between 1972 and 2021 was analysed using Landsat satellite images and the Digital Shoreline Analysis System (DSAS v.5.0), and adaptation strategies were revealed through field observations. The End Point Rate (EPR), Linear Regression Rate (LRR), and Weighted Linear Regression (WLR) were used to estimate the rate of change, whereas the Net Shoreline Movement (NSM) and Shoreline Change Envelope (SCE) were used to calculate the distances of change. The coefficient of shoreline armouring (K) index was used to evaluate the grade of artificial or human interventions on the coast. The erosion rates for the EPR (− 107.6 m/year, or 95.0%) and LRR (− 75.7 m/year, or 99.3%) were higher than the accretion rates of (28.5 m/year, or 5.0%) and (33.6 m/year, or 0.7%), respectively. The NSM recorded maximum erosion (− 14,080 to − 10,840 m) and accretion (1107 to 2135 m) with an average distance of − 4943.1 m. The SCE estimated a maximum (14,080.5 m) and minimum (813.8 m) distances with an average distance change of 5557.9 m. The central coast experienced erosion at average rates of − 119.0 m/yr, − 89.6 m/yr, and − 94.0 m/yr, according to EPR, LRR, and WLR statistics. The eastern coast observed lower erosion rates than the central coast, with rates of − 75.3 m/yr, − 53.2 m/yr, and − 41.9 m/yr for the EPR, LRR, and WLR statistics, respectively. Since 1972, there has been a significant increase in artificial coastal structures on Ghana's central and eastern coasts. The central coast has reached a maximal level of shoreline armouring index, while the eastern coast has reached a minimal level. Although hard protective measures have been implemented on most parts of the coast for adaptation, improved policy initiatives on soft and nature-based protection measures are encouraged based on their favourable ecological impact and economic effectiveness. [ABSTRACT FROM AUTHOR]

• Automating wildlife monitoring with wireless 4G cameras and end-to-end data streams. • Remote monitoring of sensor performance, API handling and automated task management. • Deep learning for automated identification of focal species and human detection. • Total cost saving of >40 % through automation, AI and less regular site visits. • Enabling technologies allow scaling-up of a cost-efficient biodiversity monitoring. Modern approaches with advanced technology can automate and expand the extent and resolution of biodiversity monitoring. We present the development of an innovative system for automated wildlife monitoring in a coastal Natura 2000 nature reserve of the Netherlands with 65 wireless 4G wildlife cameras which are deployed autonomously in the field with 12 V/2A solar panels, i.e. without the need to replace batteries or manually retrieve SD cards. The cameras transmit images automatically (through a mobile network) to a sensor portal, which contains a PostgreSQL database and functionalities for automated task scheduling and data management, allowing scientists and site managers via a web interface to view images and remotely monitor sensor performance (e.g. number of uploaded files, battery status and SD card storage of cameras). The camera trap sampling design combines a grid-based sampling stratified by major habitats with the camera placement along a traditional monitoring route, and with an experimental set-up inside and outside large herbivore exclosures. This provides opportunities for studying the distribution, habitat use, activity, phenology, population structure and community composition of wildlife species and allows comparison of traditional with novel monitoring approaches. Images are transferred via application programming interfaces to external services for automated species identification and long-term data storage. A deep learning model for species identification was tested and showed promising results for identifying focal species. Furthermore, a detailed cost analysis revealed that establishment costs of the automated system are higher but the annual operating costs much lower than those for traditional camera trapping, resulting in the automated system being >40 % more cost-efficient. The developed end-to-end data pipeline demonstrates that continuous monitoring with automated wildlife camera networks is feasible and cost-efficient, with multiple benefits for extending the current monitoring methods. The system can be applied in open habitats of other nature reserves with mobile network coverage. [ABSTRACT FROM AUTHOR]